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Confluent Certified Developer for Apache Kafka (CCDAK) Certification Examination is a rigorous and comprehensive certification exam designed to test the skills and knowledge of developers working with Apache Kafka. Confluent, the company behind Apache Kafka, offers this certification to help organizations identify and hire qualified developers who can build and maintain Kafka-based applications.
The CCDAK exam is a comprehensive test that covers various aspects of the Kafka platform, including Kafka architecture, configuration, monitoring, performance tuning, and security. CCDAK examination measures one's ability to use Kafka to handle real-time data feeds, messaging, and stream processing. CCDAK exam is designed to assess one's understanding of the platform and their ability to apply their knowledge to solve real-world problems.
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The CCDAK exam covers a wide range of topics, including Kafka architecture, data modeling, stream processing, and security. CCDAK examination comprises multiple-choice questions and hands-on exercises that require the candidate to demonstrate their practical knowledge of Kafka. Successful completion of the CCDAK Exam certifies that the individual has the expertise to design, build, and maintain Kafka-based solutions, making them a valuable asset to any organization that uses Kafka for data processing and messaging.
NEW QUESTION # 76
(You create an Orders topic with 10 partitions.
The topic receives data at high velocity.
Your Kafka Streams application initially runs on a server with four CPU threads.
You move the application to another server with 10 CPU threads to improve performance.
What does this example describe?)
Answer: A
Explanation:
This scenario describes vertical scaling, as defined in the Apache Kafka Streams documentation and general distributed systems terminology. Vertical scaling refers to adding more resources (CPU, memory, or disk) to a single machine to increase processing capacity.
In this example, the Kafka Streams application is moved from a server with four CPU threads to one with ten CPU threads, increasing the available compute resources without increasing the number of application instances. Kafka Streams can take advantage of additional CPU threads through its num.stream.threads configuration, allowing more partitions to be processed in parallel within the same application instance.
Horizontal scaling (also known as scaling out) would involve adding more application instances across multiple servers, not upgrading a single server. Option D is therefore incorrect, and Option C is not a recognized scaling term.
Thus, this example clearly illustrates vertical scaling, where performance is improved by increasing the capacity of a single node.
NEW QUESTION # 77
Match the topic configuration setting with the reason the setting affects topic durability.
(You are given settings like unclean.leader.election.enable=false, replication.factor, min.insync.replicas=2)
Answer:
Explanation:
* unclean.leader.election.enable=false# Prevents data loss by only considering in-sync replicas when rebalancing.
* replication.factor# Specifies how many redundant copies of partitions are distributed across brokers.
* min.insync.replicas=2# Sets the standard for the number of partition instances that must keep up with the latest committed message.
* unclean.leader.election.enable=false ensures that onlyin-sync replicascan be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.
* replication.factor defineshow many brokerswill maintain copies of each partition, directly impacting durability and availability.
* min.insync.replicas determineshow many replicas must acknowledgea write when acks=all is used, enforcing write durability.
Reference:Apache Kafka Topic Configuration Documentation
NEW QUESTION # 78
How would you describe a connector in ksqlDB?
Answer: C
NEW QUESTION # 79
Your application is consuming from a topic with one consumer group.
The number of running consumers is equal to the number of partitions.
Application logs show that some consumers are leaving the consumer group during peak time, triggering a rebalance. You also notice that your application is processing many duplicates.
You need to stop consumers from leaving the consumer group.
What should you do?
Answer: A
Explanation:
If the consumer fails to send heartbeats in time (due to being slow under load), it is considereddead, causing a rebalance. Increasing session.timeout.ms gives the consumermore time to process messages and send heartbeats, preventing premature removal.
FromKafka Docs > Heartbeats and Failures:
"If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance." Reducing max.poll.records mightslow consumption further. More consumers won't help since each partition already has a dedicated consumer.
Reference:Apache Kafka Consumer Configs > session.timeout.ms
NEW QUESTION # 80
What is a consequence of increasing the number of partitions in an existing Kafka topic?
Answer: B
Explanation:
Increasing partitions increases parallelism, but also means:
Consumers in a group may have to handle more partitions, especially if the number of consumers is lower than the number of partitions.
This can result in increased lag, especially under high load.
From Kafka Topic Management Docs:
"Increasing the number of partitions increases consumer work, and if consumers can't keep up, lag can accumulate." A is false: existing data is not redistributed.
B is false: records with the same key always map to the same partition based on hash.
D is not directly impacted by the partition count.
Reference: Kafka Topic Management > Adding Partitions
NEW QUESTION # 81
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